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Neural nets versus conventional techniques in credit scoring in Egyptian banking

Abdou, HAH; Pointon, J; El-Masry, A


HAH Abdou

J Pointon

A El-Masry


Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank’s personal loans’ data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.


Abdou, H., Pointon, J., & El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert systems with applications, 35(3), 1275-1292.

Journal Article Type Article
Publication Date Oct 1, 2008
Deposit Date Dec 3, 2009
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 35
Issue 3
Pages 1275-1292
Keywords Neural nets; Conventional techniques; Banking; Credit scoring
Publisher URL

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